Proving Incentivized Tweets Work with the Twitter Streaming API

Word of mouth recommendations are among the most effective ways to get new users. But they can be an elusive element of a marketing mix. Aye Moah (our Chief of Product) wrote a piece a few years ago on how Boomerang uses the paywall to have a positive interaction with users. But we also use it to encourage customers to tweet about Boomerang in exchange for more message credits (which allow users to snooze or schedule emails for later).

Wanting to know whether such incentivized tweets were effective, we completed a month-long experiment where we tracked paywall tweets from the moment they were tweeted using Twitter’s Streaming API.

We found that incentivized tweets are an effective way to generate genuine word of mouth recommendations that reach thousands of real users!

It’s impossible to track every paywall tweet (users might edit the wording or keep their tweets private), but we tracked 198 tweets in July that contained the default, pre-populated text “Been using Boomerang…”

The vast majority of users were tweeting on genuine, established accounts, not on throwaway accounts or those with few followers. Handles behind the tweets averaged 992 followers, and four tweets came from accounts with more than 10,000 followers!

Loyal users: 86% of users kept their recommendation up after getting free credits

Tweets about Boomerang originated on genuine user accounts, but did they pass the test of time? Twitter’s API allowed us to see if and when a tweet was deleted by looking it up every 5 minutes. We were thus able to see how many users reneged (deleted their tweet) once they got their free message credits.

As someone who deleted my last incentivized tweet after I got my reward (sorry, American Airlines), I was a bit cynical. But it turns out that the vast majority of Boomerang’s users are better people than me! They were genuinely happy to share their love of Boomerang with followers, keeping their tweets up long after they cashed out on their free credits. 86% of the paywall tweets we saw in July were still visible two weeks after they were first tweeted.

And so, man (Boomerang users, at least) is inherently good!

Genuine recommendations: users personalized tweets and made them stronger.

We didn’t expect to find many paywall tweets that differed from the pre-populated text (“Been using Boomerang boomeranggmail.com to schedule messages in Gmail or make emails go away until I’m ready. Highly recommended.”), as we were only searching for tweets containing the phrase “Been using Boomerang.” But we found quite a number of tweets from users who edited or added onto the default tweet text to make it even more effective.

Users took extra time to shape the tweet in a way that better resonated with their audience by adding recommendations in a second language, or matching the persona of their Twitter handle (e.g., conferences changed “I” to “We”).

And some users even decided the tweet didn’t reflect just how much they enjoyed using Boomerang. Users increased the excitement of their tweet ( “Highly recommended.” became “Highly recommended!”) or declared that Boomerang was the best email app that they had gotten.

It was awesome seeing users take the time and effort to change the default tweet text into an even stronger recommendation of our app. Users didn’t just want free credits, they genuinely wanted to craft a tweet that reflected their excitement for the app or better reach followers who spoke a different language.

TL;DR: Incentivized tweets work; Twitter API yields useful metrics

The main takeaway from our project was that incentivized tweets are an effective way to get word of mouth recommendations out to thousands of people. Users are genuine (they use their real handles), loyal (they don’t delete the tweet even after they get their freebie), and take ownership behind what they are tweeting (by personalizing and strengthening the tweet).

We were also thrilled that Twitter’s API offered us a way to measure the effectiveness of a marketing technique quantitatively. We barely scratched the surface with the information it provided, which also included user language, location, and more. You can do so much with this data, like visualize all the places tweets come from!

Locations of users who tweeted a paywall tweet in July (Twitter profile location fields geocoded by Mapcustomizer.com)